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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Related Experiment Video

Updated: Jul 6, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression

Yin-Jing Tien1, Yun-Shien Lee, Han-Ming Wu

  • 1Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan. gary@stat.sinica.edu.tw

BMC Bioinformatics
|March 28, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel gene expression profiling method that combines hierarchical clustering tree (HCT) and singular value decomposition (SVD) for improved data visualization and analysis. The new approach enhances local clustering while identifying global trends for better biomedical insights.

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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Last Updated: Jul 6, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Hierarchical clustering tree (HCT) and singular value decomposition (SVD) are common methods for gene expression profiling.
  • HCT excels at local patterns, while SVD identifies global structures.

Purpose of the Study:

  • To develop an improved gene expression profiling method by integrating HCT and SVD.
  • To enhance visualization and analysis of gene expression data.

Main Methods:

  • A novel flipping mechanism for HCT using rank-two ellipse (R2E) seriation as a reference.
  • Integration of HCT's local clustering with R2E's global grouping properties.

Main Results:

  • The proposed method preserves coherent local clusters and identifies global grouping trends.
  • The algorithm offers a superior clustering and visualization environment for gene expression profiles.

Conclusions:

  • The new method shows better statistical properties and provides more meaningful biomedical insights.
  • Sorted proximity matrices aid in understanding complex gene expression structures.